Embodiments of the invention relate generally to diagnostic imaging and, more particularly, to a system and method of iterative image reconstruction for computed tomography.
Typically, in computed tomography (CT) imaging systems, an x-ray source emits a fan-shaped beam toward a subject or object, such as a patient or a piece of luggage. Hereinafter, the terms “subject” and “object” shall include anything capable of being imaged. The beam, after being attenuated by the subject, impinges upon an array of radiation detectors. The intensity of the attenuated beam radiation received at the detector array is typically dependent upon the attenuation of the x-ray beam by the subject. Each detector element of the detector array produces a separate electrical signal indicative of the attenuated beam received by each detector element. The electrical signals are transmitted to a data processing system for analysis which ultimately produces an image.
Generally, the x-ray source and the detector array are rotated about the gantry within an imaging plane and around the subject. X-ray sources typically include x-ray tubes, which emit the x-ray beam at a focal point. X-ray detectors typically include a collimator for collimating x-ray beams received at the detector, a scintillator for converting x-rays to light energy adjacent the collimator, and photodiodes for receiving the light energy from the adjacent scintillator and producing electrical signals therefrom.
Typically, each scintillator of a scintillator array converts x-rays to light energy. Each scintillator discharges light energy to a photodiode adjacent thereto. Each photodiode detects the light energy and generates a corresponding electrical signal. The outputs of the photodiodes are then transmitted to the data processing system for image reconstruction. Alternatively, x-ray detectors may use a direct conversion detector, such as a CZT detector, in lieu of a scintillator.
CT systems typically use analytical algorithms such as a filtered back-projection algorithm to reconstruct images from the acquired image data. Alternatively, an iterative technique may be used for reconstruction to improve image quality. For example, a model-based iterative reconstruction algorithm may be used to estimate an image based on pre-determined models of the CT system, the acquired projection data, and the reconstructed image such that the reconstructed image best fits the image data.
Conventional iterative algorithms typically assume that voxel values are constant over time. However, in clinical applications, this assumption may be violated due to various reasons, such as patient motion, breathing, peristalsis, heartbeats, or contrast agent flow in perfusion studies, for example. In such scenarios, iterative algorithms often generate artifacts that are more pronounced and may extend farther from the source of motion than in analytical reconstruction algorithms.
Therefore, it would be desirable to design a system and method of iterative image reconstruction that overcome the aforementioned drawbacks.